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Proceedings of the 2014 SIAM International Conference on Data Mining

Density-Based Clustering Validation


One of the most challenging aspects of clustering is validation, which is the objective and quantitative assessment of clustering results. A number of different relative validity criteria have been proposed for the validation of globular, clusters. Not all data, however, are composed of globular clusters. Density-based clustering algorithms seek partitions with high density areas of points (clusters, not necessarily globular) separated by low density areas, possibly containing noise objects. In these cases relative validity indices proposed for globular cluster validation may fail. In this paper we propose a relative validation index for density-based, arbitrarily shaped clusters. The index assesses clustering quality based on the relative density connection between pairs of objects. Our index is formulated on the basis of a new kernel density function, which is used to compute the density of objects and to evaluate the within- and between-cluster density connectedness of clustering results. Experiments on synthetic and real world data show the effectiveness of our approach for the evaluation and selection of clustering algorithms and their respective appropriate parameters.

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cover image Proceedings
Proceedings of the 2014 SIAM International Conference on Data Mining
Pages: 839 - 847
Editors: Mohammed Zaki, Qatar Computing Research Institute (QCRI) and Rensselaer Polytechnic Institute (RPI) USA, Zoran Obradovic, Temple University, Philadelphia, Pennsylvania, Pang Ning Tan, Michigan State University, East Lansing, Michigan, Arindam Banerjee, University of Minnesota, Minneapolis, Minnesota, Chandrika Kamath, Lawrence Livermore National Laboratory, Livermore, California, and Srinivasan Parthasarathy, The Ohio State University, Columbus, Ohio
ISBN (Online): 978-1-611973-44-0


Published online: 28 April 2014



Ricardo J. G. B. Campello

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